Abstract
Introduction.The estimation of efficiency of methods and algorithms for solving optimization problems with a vector criterion and a set of nonlinear constraints is considered. The approach that allows proceeding to an optimization problem with a single objective function (i.e., an unconditional optimization problem) after equivalent transformations is described. However, the objective function obtained in this way has properties (nonlinearity, multimodality, ravine, high dimension) that do not allow classical methods to be used to solve it. The presented work objective is to develop hybrid methods, based on combinations of the algorithms inspired by wildlife with other approaches (gravitational and gradient) for the solution to this problem.Materials and Methods.New methods to solve the specified problem are developed. A computer experiment was conducted on a number of test functions; its analysis was performed, showing the efficiency of various combinations on various functions.Research Results.The efficiency of hybrid algorithms that combine the following approaches is evaluated: genetic and immune; methods of swarm intelligence and genetic and immune; immune and swarm with gravity and gradient.Discussion and Conclusions.The hybrid algorithms in optimization problems are studied. In particular, decisions can be made on their basis under the management of compound objects in the military and industrial sectors, in the creation of innovative projects related to the digital economy. It is established that the type of the objective function affects the result much more than the combination of algorithms.
Highlights
The estimation of efficiency of methods and algorithms for solving optimization problems with a vector criterion and a set of nonlinear constraints is considered
The approach that allows proceeding to an optimization problem with a single objective function after equivalent transformations is described
The objective function obtained in this way has properties that do not allow classical methods to be used to solve it
Summary
К вопросу эффективности методов и алгоритмов решения оптимизационных задач с учетом специфики целевой функции*. Ростов-на-Дону, Российская Федерация 4 Краснодарское высшее военное училище имени генерала армии С. 1,2,3Don State Technical University, Rostov-on-Don, Russian Federation 4Krasnodar Higher Military School named after General of the Army S. Статья посвящена оценке эффективности методов и алгоритмов решения оптимизационных задач с векторным критерием и системой нелинейных ограничений. Цель представленного исследования — разработать для решения данной задачи гибридные методы, основанные на комбинациях алгоритмов, инспирированных живой природой, с другими подходами (гравитационным и градиентным). Созданы новые методы для решения указанной задачи. Которые комбинируют следующие подходы: генетический с иммунным; методы роевого интеллекта с генетическими и иммунными; иммунные и роевые с гравитационным и градиентным. Установлено, что вид целевой функции влияет на результат гораздо более существенно, чем комбинация алгоритмов
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